Image binarization is a process of converting a grey scale image to a black and white image. A traditional way of image binarization involves selecting a global pixel threshold for an entire image and classifying all pixels above the threshold as white and all other pixels as black. However, use of a global threshold for binarization of an entire image may result in suppression of local variations within the image that may contribute towards the information content within the image.
Certain image binarization techniques determine a local threshold using a window or region around each pixel of the image. Further, depending on whether the threshold is to be used for the center pixel of the window or for all the pixels in the window, the image binarization may be performed on a pixel-by-pixel basis where each pixel may have a calculated threshold value, or on a region-by-region basis where all pixels in a region or window have same threshold value.
Typically, local thresholds are determined for specific areas of the images based upon certain assumptions such as size of objects in the image, noise levels and other image properties. However, it is difficult to accurately estimate local thresholds for different areas of the image. Furthermore, local thresholds may work well only for specific types of images for which they are designed.